Effect of forage conservation method on plasma lipids, mammary lipogenesis, and milk fatty acid composition in lactating cows fed diets containing a 60:40 forage-to-concentrate ratio

The effects of forage conservation method on plasma lipids, mammary lipogenesis, and milk fat were examined in 2 complementary experiments. Treatments comprised fresh grass, hay, or untreated (UTS) or formic acid treated silage (FAS) prepared from the same grass sward. Preparation of conserved forages coincided with the collection of samples from cows fed fresh grass. In the first experiment, 5 multiparous Finnish Ayrshire cows (229 d in milk) were used to compare a diet based on fresh grass followed by hay during 2 consecutive 14-d periods, separated by a 5-d transition during which extensively wilted grass was fed. In the second experiment, 5 multiparous Finnish Ayrshire cows (53 d in milk) were assigned to 1 of 2 blocks and allocated treatments according to a replicated 3 × 3 Latin square design, with 14-d periods to compare hay, UTS, and FAS. Cows received 7 or 9 kg/d of the same concentrate in experiments 1 and 2, respectively. Arterial concentrations of triacylglycerol (TAG) and phospholipid were higher in cows fed fresh grass, UTS, and FAS compared with hay. Nonesterified fatty acid (NEFA) concentrations and the relative abundance of 18:2n-6 and 18:3n-3 in TAG of arterial blood were also higher in cows fed fresh grass than conserved forages. On all diets, TAG was the principle source of fatty acids (FA) for milk fat synthesis, whereas mammary extraction of NEFA was negligible, except during zero-grazing, which was associated with a lower, albeit positive calculated energy balance. Mammary FA uptake was higher and the synthesis of 16:0 lower in cows fed fresh grass than hay. Conservation of grass by drying or ensiling had no influence on mammary extraction of TAG and NEFA, despite an increase in milk fat secretion for silages compared with hay and for FAS than UTS. Relative to hay, milk fat from fresh grass contained lower 12:0, 14:0, and 16:0 and higher S3,R7,R11,15-tetramethyl-16:0, cis-9 18:1, trans-11 18:1, cis-9,trans-11 18:2, 18:2n-6, and 18:3n-3 concentrations. Even though conserved forages altered mammary lipogenesis, differences in milk FA composition were relatively minor, other than a higher enrichment of S3,R7,R11,15-tetramethyl-16:0 in milk from silages compared with hay. In conclusion, differences in milk fat composition on fresh grass relative to conserved forages were associated with a lower energy balance, increased uptake of preformed FA, and decreased synthesis of 16:0 de novo in the mammary glands, in the absence of alterations in stearoyl-coenzyme A desaturase activity.

An analytical method for capacity dimensioning of WCDMA with high speed wireless link

Capacity dimensioning is one of the key problems in wireless network planning. Analytical and simulation methods are usually used to pursue the accurate capacity dimensioning of wireless network. In this paper, an analytical capacity dimensioning method for WCDMA with high speed wireless link is proposed based on the analysis on relations among system performance and high speed wireless transmission technologies, such as H-ARQ, AMC and fast scheduling. It evaluates system capacity in closed-form expressions from link level and system level. Numerical results show that the proposed method can calculate link level and system level capacity for WCDMA system with HSDPA and HSUPA.

GSHMC: an efficient method for molecular simulation

The hybrid Monte Carlo (HMC) method is a popular and rigorous method for sampling from a canonical ensemble. The HMC method is based on classical molecular dynamics simulations combined with a Metropolis acceptance criterion and a momentum resampling step. While the HMC method completely resamples the momentum after each Monte Carlo step, the generalized hybrid Monte Carlo (GHMC) method can be implemented with a partial momentum refreshment step. This property seems desirable for keeping some of the dynamic information throughout the sampling process similar to stochastic Langevin and Brownian dynamics simulations. It is, however, ultimate to the success of the GHMC method that the rejection rate in the molecular dynamics part is kept at a minimum. Otherwise an undesirable Zitterbewegung in the Monte Carlo samples is observed. In this paper, we describe a method to achieve very low rejection rates by using a modified energy, which is preserved to high-order along molecular dynamics trajectories. The modified energy is based on backward error results for symplectic time-stepping methods. The proposed generalized shadow hybrid Monte Carlo (GSHMC) method is applicable to NVT as well as NPT ensemble simulations.

A nonparametric ensemble transform method for Bayesian inference

Many applications, such as intermittent data assimilation, lead to a recursive application of Bayesian inference within a Monte Carlo context. Popular data assimilation algorithms include sequential Monte Carlo methods and ensemble Kalman filters (EnKFs). These methods differ in the way Bayesian inference is implemented. Sequential Monte Carlo methods rely on importance sampling combined with a resampling step, while EnKFs utilize a linear transformation of Monte Carlo samples based on the classic Kalman filter. While EnKFs have proven to be quite robust even for small ensemble sizes, they are not consistent since their derivation relies on a linear regression ansatz. In this paper, we propose another transform method, which does not rely on any a priori assumptions on the underlying prior and posterior distributions. The new method is based on solving an optimal transportation problem for discrete random variables. © 2013, Society for Industrial and Applied Mathematics

Reading the world through words: cultural themes in heritage Chinese language textbooks

This paper explores the social and cultural knowledge embedded in the textbooks for language and literacy education in a Chinese heritage language school, the Zhonguo School, in Montreal, Quebec, Canada. It examines how Chinese language arts textbooks introduce the child reader to cultural knowledge considered legitimate and valued in China as well as in Chinese diasporan communities. Furthermore, it looks at the construction of cultural knowledge in Chinese language textbooks in relation to the mainstream ideology to which immigrant children are exposed in and out of mainstream school classrooms. It looks at how the power relationship between legitimate cultural knowledge in majority and minority contexts is established and to what extent it affects language minority students' literacy practices in mainstream school and heritage language school contexts. Data sources are the Chinese textbooks used from kindergarten to Grade 5 in a Chinese heritage language school.

A finite element method for nonlinear elliptic problems

We present a Galerkin method with piecewise polynomial continuous elements for fully nonlinear elliptic equations. A key tool is the discretization proposed in Lakkis and Pryer, 2011, allowing us to work directly on the strong form of a linear PDE. An added benefit to making use of this discretization method is that a recovered (finite element) Hessian is a byproduct of the solution process. We build on the linear method and ultimately construct two different methodologies for the solution of second order fully nonlinear PDEs. Benchmark numerical results illustrate the convergence properties of the scheme for some test problems as well as the Monge–Amp`ere equation and the Pucci equation.

A finite element method for second order nonvariational elliptic problems

We propose a numerical method to approximate the solution of second order elliptic problems in nonvariational form. The method is of Galerkin type using conforming finite elements and applied directly to the nonvariational (nondivergence) form of a second order linear elliptic problem. The key tools are an appropriate concept of “finite element Hessian” and a Schur complement approach to solving the resulting linear algebra problem. The method is illustrated with computational experiments on three linear and one quasi-linear PDE, all in nonvariational form.

A modified PNN algorithm with optimal PD modeling using the orthogonal least squares method

In this paper a modified algorithm is suggested for developing polynomial neural network (PNN) models. Optimal partial description (PD) modeling is introduced at each layer of the PNN expansion, a task accomplished using the orthogonal least squares (OLS) method. Based on the initial PD models determined by the polynomial order and the number of PD inputs, OLS selects the most significant regressor terms reducing the output error variance. The method produces PNN models exhibiting a high level of accuracy and superior generalization capabilities. Additionally, parsimonious models are obtained comprising a considerably smaller number of parameters compared to the ones generated by means of the conventional PNN algorithm. Three benchmark examples are elaborated, including modeling of the gas furnace process as well as the iris and wine classification problems. Extensive simulation results and comparison with other methods in the literature, demonstrate the effectiveness of the suggested modeling approach.

Diverging from the dominant discourse- some implications of conflicting subject understandings in the education of teachers

This paper addresses beginning teachers thinking about the nature and purposes of their subject and the impact of this on their practice. Individual qualitative interviews were undertaken with 11 history teachers at the beginning of their teaching careers. Data was analysed using writing as the method of analysis and revealed that teachers whose thinking was at odds with dominant discourses, for example in the form of a national curriculum, encountered difficulties embracing pedagogies and aspects of the curriculum that do not accord with their own deep-seated beliefs, demonstrating a need for the initial training and professional development of teachers to forefront consideration of subject understandings.

The capability-affordance model: a method for analysis and modelling of capabilities and affordances

Existing capability models lack qualitative and quantitative means to compare business capabilities. This paper extends previous work and uses affordance theories to consistently model and analyse capabilities. We use the concept of objective and subjective affordances to model capability as a tuple of a set of resource affordance system mechanisms and action paths, dependent on one or more critical affordance factors. We identify an affordance chain of subjective affordances by which affordances work together to enable an action and an affordance path that links action affordances to create a capability system. We define the mechanism and path underlying capability. We show how affordance modelling notation, AMN, can represent affordances comprising a capability. We propose a method to quantitatively and qualitatively compare capabilities using efficiency, effectiveness and quality metrics. The method is demonstrated by a medical example comparing the capability of syringe and needless anaesthetic systems.